sample size large enough to provide detailed data for 13 states. It is difficult for many states to adopt this methodology, primarily for budgetary reasons. Norton’s experience in New Hampshire is evidence of this; his budget allowed him to use only a stripped-down version of this survey.

The State Health Access Data Assistance Center at the University of Minnesota has been organized to provide assistance to states in dealing with these issues. The center is funded by a grant from the Robert Wood Johnson Foundation to provide technical assistance to states that are interested in collecting relevant data for state health policy. Lynn Blewett described what states want from national surveys. The list includes data that are representative of the individual state; a sample size that is large enough to provide valid and reliable estimates; a survey design that produces policy-relevant information; timely and routine release of data; and access to micro data for further state-specific analyses.

The effect of missing data in surveys can be a critical issue in deriving estimates from surveys. Panel member Paul Newacheck pointed out that questions on income, for example, are known to have large nonresponse rates in some of the major national surveys. He raised the question of how much imputation is going into the microsimulation models that produce estimates on insurance eligibility and what effect this might have on the estimates. If as much as a quarter of the data are imputed, this could have a substantial effect on the validity of the analysis. The discussion that followed provided no direct answer to the question, but some of the presenters stated that when they attempted to compare survey results, they found considerable similarity. This seemed to give them confidence in the use of the data from the surveys, despite high levels of missing data.


Several of the workshop participants pointed out that data definitions are not, in general, standard across states or across databases of related federal programs. The criteria for eligibility for SCHIP vary considerably among the states, not only due to the differing income limits for SCHIP and Medicaid among the states, but also due to how income is defined for determining eligibility. Perhaps the most detailed set of factors for determining income is that used in the National Survey of America’s Families. At the other end of the scale, several states use self-declaration of income to determine eligibility with considerable variation in the extent of the guidelines that are given the applicant on what to include as income. Superim

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